Five Things Co-CxO Actually Delivers 

Wade Wyant on July 18, 2026

By Wade Wyant, Founder, REDEGADES.AI 

When leaders ask me what Co-CxO actually does for a business, I don’t lead with technology. I lead with the five things every CEO is measured on: time, money, growth, competition, and return on investment. If your AI investment doesn’t move those five numbers, it doesn’t matter how good it looks in a demo. 

Here’s what Co-CxO actually delivers. 

The Hours Worth Reclaiming 

Your leadership team is burning time on work that a well-trained AI should handle. Research, analysis, briefings, prep work before every meeting. These are high-effort, low-leverage activities that eat into the hours your executives should spend on decisions only they can make. 

Co-CxO handles that preparation automatically. It knows your business, your data, and your strategic priorities. It doesn’t search the internet for generic answers. It reaches into your company’s own information and comes back with something actually useful. Your executives get hours back every week. They don’t spend those hours differently. They spend them better. 

The Price of Not Knowing 

Every leadership team makes bad decisions. That isn’t a criticism; it’s reality. The question is how often, and how expensive. Bad decisions carry a real price tag: the wrong hire, the missed market signal, the overconfident bet on a product no one wanted. Most of those decisions could have been caught earlier if someone had surfaced the right information at the right moment. 

Co-CxO is built to do exactly that. When your AI is trained on your company’s own data, your strategy, your history, and your decision patterns, it can flag what your leadership team isn’t seeing. It’s not a crystal ball. But it’s a second mind that doesn’t have an ego, doesn’t get tired, and doesn’t have a reason to tell you what you want to hear. 

Think of it this way: your AI investment is a risk management engine. Every bad decision it helps you avoid is money protected. Every blind spot caught early is a better decision waiting to happen. 

“The most expensive decisions are the ones nobody saw coming. Co-CxO is built to see them first.” 

Scale Without Starting Over 

The traditional answer to growth is hiring. You need more capacity, so you add more people. More senior people, more expensive people, people who then need onboarding and management before they contribute anything. It works, eventually. But it’s slow, expensive, and fragile. 

Co-CxO changes that equation. One of our clients navigated a 4.5x growth sprint. They did add to their leadership team as they scaled — that kind of growth requires it. But Co-CxO meant every new leader could contribute faster, stay aligned with where the company was headed, and build on the institutional knowledge already in the system rather than starting from scratch. Growth happened. The team didn’t have to reinvent itself every time it got bigger. 

That’s what I mean when I say AI is a leadership multiplier. Not a task robot. A multiplier. The difference matters more than most leaders realize until they’ve seen it work. 

The Leaders Who See It Coming 

Most leadership teams are looking at lagging indicators. Revenue that already happened. Problems that already landed. Data that describes yesterday. That’s not strategy. That’s a rearview mirror. 

Co-CxO analyzes your data and your trajectory to give your leadership team a predictive edge. It reads signals in your business before they become numbers you have to explain in a board meeting. It connects dots your team doesn’t have time to connect manually. The leaders who move now will spend the next several years reacting less and leading more. The ones who wait will keep playing catch-up with competitors who already made the move. 

The Investment That Compounds 

Here’s something about software that most people don’t say out loud: it depreciates. You buy it, implement it, and over time it gets stale. The vendor updates it. You retrain your team. The edge you had in year one is table stakes by year three. 

Co-CxO works the opposite way. The longer it runs inside your business, the smarter it gets. Every meeting it captures, every decision it learns, every strategy document it ingests makes the model more valuable. It compounds. It doesn’t depreciate; it appreciates. That isn’t a feature. That’s a fundamentally different relationship with your technology investment. 

“Unlike software that depreciates, Co-CxO compounds. The longer it runs, the smarter it gets — and the greater the return.” 

Five outcomes. One platform. All of it built around the way your leadership team actually thinks, decides, and leads. That’s the promise of Co-CxO. Not more technology. Better outcomes from the team you already have. 

Stop Protecting Your Data from the Wrong Enemy 

Wade Wyant on July 1, 2026

Your AI paranoia is costing you more than a data breach ever would. 

There is a conversation happening in boardrooms across the country, and it is costing businesses millions of dollars in lost productivity, missed decisions, and competitive ground surrendered. It goes like this: I am not sure we should put that into AI. What if someone gets it? 

I have spent my career in cybersecurity. Top-secret clearance. Decades running businesses built around keeping data safe. And I am telling you flat out: this particular fear is almost entirely unfounded. 

Here is what is actually going on, why the fear exists, why it is mostly wrong, and where you should actually be spending your worry. 

First, a Quick History Lesson: Security by Obscurity 

Early cybersecurity thinking leaned on a concept called security by obscurity. The idea was simple. If your data was hard enough to find and hard enough to piece together, attackers would move on. Be obscure enough and you were protected. 

It worked until it did not. Big data changed everything. Suddenly your size did not protect you. Every transaction, every login, every record had value to someone. We spent years telling companies that security by obscurity was dead. You cannot hide anymore. Everyone is a target. Everyone needs real protection. 

That was true. In the traditional cybersecurity world, it still is true. But something unexpected happened with the rise of LLMs, and almost nobody is talking about it. 

The Plot Twist: Security by Obscurity Is Back, and It Is Working 

In the context of large language models, your data is so obscure, so unremarkable, and so lost in the ocean of information flooding through these systems that the probability of it being extracted, identified, and weaponized against you is effectively zero. 

A few years ago, the early AI companies absolutely wanted your data. Training on real-world conversations and inputs was how these models got smarter. ChatGPT’s early days almost certainly included data from users who never intended to contribute to a training set. 

That was then. Today the volume of information running through these models is staggering. Your quarterly financials, your pricing conversations, your strategic planning notes are a drop in the Pacific Ocean. The model is not sitting there waiting to surface your pricing strategy to your top competitor. It is processing billions of inputs from around the world. You are not interesting enough for it to notice. 

“Your data is not as interesting as your fear says it is.” 

I Asked for Proof. Nobody Had Any. 

The rumors are everywhere. I heard about a company in my industry that fed their numbers into ChatGPT and now their competitors have them. I always respond the same way: show me the specific company. Give me the person’s name. Show me the documentation. 

Nobody ever can. It is always I heard. It is always a friend of a friend. It is always a vague story with no verifiable details. 

What I believe has happened is that we have collectively constructed a paranoia that has no real evidence beneath it. And that paranoia is causing smart CEOs, people I respect enormously, to hold their companies back from one of the most powerful business tools ever created. 

Yes, You. I Am Talking Directly to You. 

Whoever you are reading this right now, I want to be honest with you. The chances that your data is of material interest to anyone training an AI model are 99.9999% against. You are not a target. The LLMs have moved way beyond the point of wanting your client list or your go-to-market strategy. We are talking about intelligence operating at a scale most of us cannot fully comprehend. 

Does that mean there is zero risk? No. Nothing is zero risk. If your data is genuinely unique, genuinely proprietary, and genuinely valuable at a level most businesses cannot imagine, then maybe you have a conversation worth having. But for the overwhelming majority of companies asking this question, the answer is: relax. 

What to Do If You Still Want Extra Protection 

If you read all that and still want a stronger wall between your data and the AI systems you use, there are real, cost-effective solutions. Private AI configurations can obscure your data before it ever reaches an LLM, so the model processes your queries without ever knowing who is behind them. Full private deployments keep everything inside your walls entirely. The Defense Department uses versions of this. Certain governments and major banks do too. 

REDEGADES.AI offers private AI solutions at a fraction of what you might expect. If peace of mind has a dollar value in your organization, this is worth exploring. Just be clear about why you want it. Not because an LLM is going to steal your strategy because it almost certainly will not. But because executive confidence, board expectations, or enterprise compliance requirements might make it the right call. 

The Real Threat Nobody in This Conversation Mentions 

Here is where I need you to lean in. 

Almost any company’s data is available right now through corporate espionage. Not through sophisticated hacking. Not through AI. Through old-fashioned human intelligence gathering that requires almost no illegal activity, carries minimal legal risk, and is happening at a frequency that would make most CEOs lose sleep if they actually looked at it. 

Corporate espionage is the threat no one wants to deal with because there is no clean software fix. It requires human vigilance, organizational discipline, and ongoing attention. It is harder than signing an enterprise AI agreement and checking a compliance box. And so most companies ignore it entirely. 

“Your biggest data risk is not in the cloud. It is probably walking past your building right now.” 

So What Should You Actually Do? 

Stop letting AI paranoia slow you down. The leaders who are winning right now are the ones asking better questions, moving faster, and getting more out of every working hour because they stopped being afraid of the tool and started mastering it. 

If you want private AI, get it. REDEGADES.AI makes it easy and affordable. If you want to protect your company’s real competitive data, start taking corporate espionage seriously. And if you are still telling yourself that feeding a question into ChatGPT is going to hand your strategy to a competitor, I respectfully ask you to re-examine that belief. 

You are not that interesting to the LLM. But your competitor thinks you are very interesting indeed. 

Bias: Build a Machine That Thinks Like You

Wade Wyant on June 30, 2026

Bias: Build a Machine That Thinks Like You 

Generic AI doesn’t know your leadership bias, but REDEGADES.AI’s bespoke and private AI is trained on your judgment, priorities, and strategy. 

Bias 

The term bias is bursting with negative connotations, but when we use the term bias, we are talking about something positive, your worldview. We are talking about the way your business operates. 

Each business has a unique way that it interacts with customers and makes itself distinctive. At REDEGADES.AI, we refer to the DNA and characteristics of a business plan as its bias. 

For example, what’s your company’s bias about employees who are underperforming in their current roles? 

a.   Retrain people rather than fire them. 

b.   Find a different role for these people in the organization. 

c.   Fire people, but give them a severance package. 

d.   Fire people without a severance package. 

Your company’s bias toward firing may not be the same as other businesses, but the impact of your bias will determine how your business makes decisions. 

One example of an easy bias is: What business methodology do you use? 

  • Scaling Up methods by Verne Harnish 
  • Jim Collins’ principles (e.g., Beyond Entrepreneurship 2.0, Flywheel) 
  • Great Game of Business by Jack Stack 
  • Entrepreneurial Operating System (EOS) 
  • Patrick Lencioni’s principles (e.g., The Five Dysfunctions of a Team, Death by Meeting) 

Some companies use one business methodology while others combine positive aspects from a few different ones. To provide the most advantages, your AI system needs to know which business methodology you use. 

For example, if you enter the book Beyond Entrepreneurship 2.0 into AI as a bias, then AI will make suggestions that align with the principles in this book. 

Inputting bias into AI will keep your business trending the way you want it to go, but what way do you want it to go? It’s important to take time to discover what aspects of the business are critically important to you. An outside company, such as REDEGADES.AI, can help you define how you want to manage your business and help you with the process of self-discovery. 

Once you have an idea of how your business should operate, you can train AI around that bias. However, you must use some moderation because you don’t want AI to be an exact copy of you. AI should be aligned with your thinking, but it should still challenge you and give you an appropriate level of counterbalance. 

As we developed this bidirectional communication plan or rhythm with AI, we noticed that we needed to introduce a bias and a mindset into AI. (When REDEGADES.AI refers to bias, we are not talking about bias against people. We are referring to a worldview where you insert your values and ideals as a company.) 

To insert the bias and mindset into AI, we asked ourselves, “What’s the history of the organization?” Many times, a company’s history will dictate where we go next. AI needs to know where a company has succeeded and failed, so it can refine its output to best meet the strengths and weaknesses of the company. When AI knows a company’s history, it becomes a much more compatible partner. 

What information does AI need to know about the structure of your business? 

  • Business history 
  • Core values 
  • Business culture 
  • Frameworks from business leaders, such as Jim Collins 
  • Company’s purpose 
  • Brand promise 
  • One-page plan 
  • BHAG (big, hairy, audacious goal) 
  • Flywheel 

Once AI understands the structure of your company, your company’s past strategies should be inputted into its system. However, this old information should be weighted so AI knows how much emphasis to put on it. Inputting old strategies is for historical context only. In the future, when AI makes recommendations and processes data, it will have this background information. However, the data should be weighted so AI will realize that this information does not have to be part of the current recommendation. 

AI must also know information about the leadership team, such as who has strong leadership skills and who doesn’t. Plus, it needs to know about the challenges team members have. That’s the bias we are building in. If you don’t add a bias, the answers from AI are too generic. 

Bias also involves industry trends and customer preferences. When AI has this data or bias, it can make better recommendations. AI must have this information about people’s preferences, strengths, and weaknesses because it is dealing with humans. It’s important to give AI the necessary data so it can work more effectively. To prove my point, if you think about the bulk of a CEO’s job, it’s dealing with humans and their problems. 

How Can AI Achieve Better Results for You and Understand Your Bias? 

  • Through reinforcement and corrective feedback 
  • Use of RAG, retrieval-augmented generation 
  • Inputting business documents into the RAG 
  • Updating and structuring information inputted into the RAG 

The Day You Build a Machine That Thinks Like You 

There’s a moment, and every CEO knows it, when you whisper to yourself, “If I could just clone myself…” We say it in the late-night hours when the inbox has mutated into a monster. We say it during those meetings where half the room speaks in circles while the other half stops speaking altogether. The whispered desire to clone yourself turns into a shout when the decisions pile up. Whether it’s strategic, financial, or people decisions, the company waits for us to think, decide, and move. 

For the longest time, that wish to clone ourselves was nothing more than a fantasy. A joke we made in the break room. A wistful sigh on the drive home. Now that AI has crossed the threshold, that desire can become a reality. 

How does this become a reality? AI becomes an amazing asset to your company when you build a machine that thinks more like YOU than any employee ever could, because it can ingest every doctrine, every preference, every bias, and every pattern that makes your leadership yours. How do you achieve an AI that thinks like you? Train it on the right things, weight the data correctly, and feed the right data in the right order. 

With this type of AI model, you’ll get a machine that doesn’t think generically or merely offer “best practices.” In other words, it won’t be a machine that thinks like Google Search with a necktie on. Instead, it will be a machine that thinks like you. 

The Myth of “Bigger is Better” 

When AI first hit mainstream consciousness, everyone chased the biggest models they could find. They wanted more parameters, tokens, and power. All of these are very important things, but a private AI system that thinks like you provides the biggest win. 

The best AI model doesn’t try to think like the universe. Instead, it tries to think like you, in your universe. 

Most people assume they have to use ChatGPT or Claude straight out of the box. They don’t realize customization is nearly limitless and far more powerful than people know. 

An AI model trained on the right doctrinal inputs becomes your most valuable technological asset. Why? It’s because alignment matters, especially when it’s aligned to your business. 

Doctrine: The Soul of Your AI 

All leaders have doctrine, even if they’ve never written it down. Some CEOs are “rip the Band-Aid off” people when it comes to performance problems. Others will nurture an underperformer for eighteen months, hoping potential finally becomes reality. Some think in EOS language or in Scaling Up language while others speak the “my gut never lies” language. Doctrine drives every meaningful leadership decision. 

Your doctrine (your real doctrine, not the one you claim) drives how you lead, how you make decisions, and how you run a company. 

If you don’t capture your doctrine, your AI will default to the doctrine of the internet, which is the doctrine of “everyone else.” Once again, you’ll be stuck with something that thinks for you instead of with you. 

Private AI models enable you to build doctrine directly into your AI. You can encode: 

  • how you hire and fire, 
  • how you handle risk, 
  • how you think about money, 
  • how you weight certain people’s opinions, 
  • how you challenge yourself, 
  • what your values are, and 
  • what you never compromise on. 

Encoding your doctrine into AI isn’t optional. Rather, your doctrine is the soul of your AI. This is how the machine starts thinking like you. 

Custom AI Learns Your Patterns 

Most leaders think of AI as a giant machine that pulls answers out of a magic cloud. Custom AI is an entirely different model focused on alignment with your business philosophy. Custom AI tries to understand you, and that’s why it’s powerful. What makes your business unique, your DNA, lives in the tiny corners of your decisions, not in the “standard operating procedures.” 

A private AI model learns your unique way of thinking, and it begins to understand: 

“Why did you pick that?” 

“Why do you always avoid this?” 

“Why do you trust that?” 

In other words, AI models can be trained to learn your bias. This is why REDEGADES.AI encourages leaders to begin implementing AI models with the leadership team. Instead of wishfully thinking, “If I could only clone myself,” why not do this now? Start with the C-suite. 

Your AI should be trained first and foremost on your way of thinking, before it ever touches the rest of the company. This is the essential first step. 

The Step Most Leaders Miss 

Here’s the thing most leaders don’t see coming. Once your leadership team each builds a great AI, you have a new problem. Every person in the room is now more confident than ever, armed by a machine that told them they were right, reasoning from different data. You walk into your most expensive meetings with a room full of better-armed arguments. Decisions don’t stick. Disagreements get sharper. And you, as CEO, can no longer tell a real strategic debate from two people fighting over whose AI had better inputs. 

Building AI that thinks like you is the foundation. It is not the finish line. The next step is making sure all of that individual intelligence connects into one shared brain. When every leader on your team is drawing from the same sandbox, the same context, the same company DNA, the false disagreements disappear. What’s left is the real conflict, the kind worth having. That’s what REDEGADES.AI calls the Decision Alignment Layer, and it’s where individual AI becomes organizational intelligence. 

The Lens That’s Costing You the AI Race

Wade Wyant on June 27, 2026

Most companies are not failing at AI because AI is hard. They are failing because they are looking at it through a lens that is twenty years old. And that lens was built from decisions that were, at the time, exactly right. 

You Made All the Right Moves. That Is the Problem. 

Over the last two decades, the business world made a massive, correct shift: from owning software to subscribing to it. Email, documents, design tools, video meetings, all in the cloud, all on a subscription, none of it running on a server in a closet down the hall. Today, somewhere between 60 and 75 percent of enterprise software is delivered this way. That number keeps climbing. 

And for most companies, outsourcing IT followed the same logic. Why hire and retain an internal IT team when a Managed Services Provider gives you better coverage, deeper expertise, and lower cost? More than 65 percent of mid-market firms operate this way now. Smart. Efficient. Right call. 

Here is the problem: two decades of smart, right decisions have a side effect. They trained the entire business world to think in one mode. Find a subscription, hand it off, move on. 

That Mode Is Now Your Biggest Barrier to AI 

There is no off-the-shelf subscription that raises the intelligence of your leadership team as a unit. 

There are plenty of tools that make you smarter as an individual. ChatGPT, Claude, Copilot. Pick your flavor. They will make you faster, sharper, more effective. But none of them are built to align the intelligence of your entire leadership team. That gap is real, and it is not solved by clicking Start Free Trial. 

When we talk to companies about building real AI capability, we hit the same wall every time. They go looking for a pricing page. When they cannot find it there, they assume it does not exist, or that it is too complicated, too expensive, too much work. It is not. It is actually the opposite. But you cannot see that if you are still looking through a twenty-year-old lens. 

The Companies Already Winning Did Not Plan for This 

Here is the counterintuitive finding: the companies most ready for private AI are the ones that never fully adopted the SaaS-everything model. Manufacturers, regional banks, specialty healthcare groups in the $50 million to $500 million range that kept developers on staff and maintained real IT infrastructure. They did it because their operational complexity demanded it, not because they saw AI coming. 

But that decision gave them something priceless: they still understand what it means to own a technology environment. That mindset, not talent, not budget, not timing, is the difference. 

The Lighter Moment 

Imagine someone in the middle of a crisis, scrambling to solve a problem with their bare hands. Someone walks up and hands them exactly the tool they need. They wave it off. Do not bother me, I am trying to fix this. 

That is not a cartoon. That is a Tuesday at most mid-market companies when AI infrastructure comes up. 

Or think of it this way: handing a lighter to someone who has only ever made fire by hand. The lighter is not complicated. Their frame of reference is. All they have to do is spin the dial and push the button, but they are staring at it, completely lost, because their entire mental model was built around a different method. Most companies are at that moment with AI right now. 

What CEOs Need to Do Differently 

The moves that got you here, SaaS, cloud, managed services, were the right moves. They are not the problem. The problem is carrying the same thinking into terrain where it no longer applies. 

Building a private AI environment fitted to your team is not a significant lift. It is not a multi-year IT project. But it requires a different frame: one where you think about owning capability, not just subscribing to it. 

But Here Is the Part the Subscription Mindset Hides Completely 

There is a cost to the wrong lens that goes deeper than missed capability. And most CEOs will not see it until it is already in the room with them. 

When every leader on your team subscribes to their own generic AI tool, a very specific thing starts happening. Each one gets sharper. Each one gets faster. Each one walks into leadership meetings more prepared and more certain than they used to be. That sounds like a win. It is not. 

Generic AI is trained on the same public data for everyone. But each leader is asking it different questions, feeding it their own context, and getting answers that confirm the way they already see the world. The CFO’s AI is building her case. The CMO’s AI is building his. By the time they get in the room together, you do not have a smarter leadership team. You have a room of better-armed arguments. The meetings get harder, not easier. The decisions take longer to stick. The CEO becomes the permanent referee between people who are each completely convinced they are right. 

“The subscription mindset does not just miss the capability. It actively creates the problem. Every individual tool your team adopts makes the alignment gap wider.” 

This is what the right lens reveals. The goal is not to get each leader a better AI tool. The goal is to give the whole team one shared intelligence: same data, same context, same foundation. So when the debate happens, it is a real debate about real strategy, not a collision between twelve people who each have a machine telling them they are correct. 

The companies that make this shift first will not just be better at AI. They will be in a different category entirely, one that their competitors cannot buy their way into from a pricing page. The lens you have been looking through has served you well. It is also the exact thing slowing you down. Change the frame. 

Why Does Custom AI Give You a Strategic Advantage?

Wade Wyant on June 27, 2026

Why Does Custom AI Give You a Strategic Advantage? 

There is a dangerous illusion happening in business right now. Executives believe they are gaining a competitive advantage simply because they are using AI, but they are mistaken. Instead, they are simply gaining speed and surface productivity. Generic AI does help companies develop better summaries, faster drafts, and improved brainstorming. 

However, companies that use generic AI and public data are not dominating their fields. In other words, they are not differentiating themselves from the competition and becoming the clear choice for their customers. Why is this happening? The same intelligence they are using is available to everyone else. 

Public AI is shared intelligence, and shared intelligence does not create strategic dominance. To gain dominance, you need structured data in a custom-built AI system. REDEGADES.AI helps you structure data and then input it into AI, so that AI understands your company’s vision, mission, and purpose. 

Public AI: The Illusion of Advantage 

Public large language models (ChatGPT, DeepSeek, Gemini) are extraordinary achievements. They are trained on vast portions of the internet and have absorbed patterns across business, language, research, and culture. When you ask them a question, they respond with statistically optimized intelligence based on global data. But global data is not your data. 

Public AI does not understand your capital structure, your long-term strategic bets, your political realities, your board expectations, or the subtle cultural nuances inside your organization. It does not understand which decisions failed and why. Also, public AI does not comprehend which risks you are willing to take and which you are constitutionally unwilling to entertain. 

When you ask public, or generic, AI for strategic guidance, it gives you what works in general. It does not give you what works for your company. Even worse, when your competitors ask the same question, they receive essentially the same intelligence. That is not an advantage. That is equality among competing organizations. 

Equality feels powerful when you are moving faster than you used to, but it does not win markets. However, custom AI does. 

Curated Data: Teaching the System Your Strategic DNA 

Curated data gives your company a competitive advantage. Instead of allowing AI to operate purely from global statistical knowledge, you deliberately feed it your organization’s strategic intelligence. That includes board decks, quarterly planning documents, KPI structures, leadership meeting transcripts, capital allocation models, long-term vision statements, customer behavior data, and operational metrics. These pieces of strategic intelligence are important, but you cannot just dump files into AI. Rather, your custom AI needs structured, accurate information. 

What matters is that your organization develops a disciplined, accessible, integrated source of truth. A system where information is not scattered across disconnected tools, but architected into a coherent intelligence layer. 

Data curation is when data is monitored on an ongoing basis to make sure that it is accurate, up-to-date, and in the correct format. Most importantly, data curation enables your AI system to be customized to understand your business. Without curation, AI guesses based on the world. With curation, AI reasons based on your company’s worldview. The difference is subtle at first. But over time, it becomes exponential. The system begins to internalize your patterns, your strategic posture, and your historical context. Custom AI no longer answers generically. It answers in alignment. That is the first step from productivity tool to strategic asset. 

Weighted Data: Encoding Authority, Bias, and Direction 

Most organizations stop at curation, or training AI with their data. They need to continue working with AI to weight the data they are inputting. Not all information inside a company carries equal authority. So, all voices should not be weighted the same. In fact, some documents and some voices in the organization carry more weight than others. 

If your AI treats every piece of information as democratically equivalent, it will produce diluted strategy. It will average conflicting ideas, smooth sharp edges, and generate safe recommendations. However, leadership is not democratic. The voice of the CEO carries more weight and authority than a newly hired employee. With custom AI, data is weighted so that AI knows which voices and documents to focus on. 

Weighted data encodes hierarchy into the intelligence layer. It tells the system which frameworks override others. It clarifies which documents represent long-term doctrine versus short-term reaction. It establishes which voices define the organization’s strategic center of gravity. This is where AI begins to move beyond retrieval and begins to think within your worldview. 

When properly structured, weighted data in AI does not simply summarize what was said in meetings. It highlights contradictions, surfaces drift from stated priorities, and identifies when execution diverges from doctrine. Custom AI becomes a mirror of leadership integrity. That is a fundamentally different capability than answering questions from the internet. 

Why REDEGADES.AI 

REDEGADES.AI is not positioned as a generic AI consultant company. Instead, we operate at the executive layer. Our focus is singular: building structured, weighted, curated intelligence systems for CxOs. We do not begin with automation. We begin with leadership. Because the constraint in most organizations is not the call center. It is the cognitive load at the top. 

We bring C-level experience into AI architecture. We understand quarterly planning rhythms, board pressure, and capital allocation tension. We even understand strategic drift. So, we structure AI around those realities. 

We are not attempting to build a moat around proprietary models. We are building architectural expertise around executive intelligence. Everyone can access public models, but very few are architecting executive intelligence. 

There Is a Second Problem Nobody Is Talking About 

Here is where the story does not end, and where most companies are about to run into something they did not see coming. 

Custom AI is the right move. Build your curated intelligence layer. Weight your data. Teach the system your strategic DNA. That is Step 1, and it is a real competitive advantage. 

Step 2 is the part almost no one has thought through yet. 

Watch what happens when every leader on your team does exactly what this article recommends. Your CFO builds a custom AI trained on her financial philosophy. Your CMO builds one trained on his market instincts. Your COO builds one aligned with her operational frameworks. Each leader is sharper, faster, and more confident than ever. Each one walks into your next leadership meeting armed by a machine that has studied their thinking, validated their assumptions, and made their case airtight. 

What you have built is not a smarter company. You have built a company of smarter individuals who agree less. 

The same technology that made each leader more effective has quietly made the leadership team harder to align. The debates get sharper. The positions get more entrenched. The meetings that should take twenty minutes take three rounds, because everyone has an AI-backed argument they believe in completely. And the CEO can no longer tell a real strategic disagreement from two smart people who simply pulled their intelligence from different places. 

“You cannot coach or referee that problem. You cannot see it clearly enough from inside the room. And it compounds every day you sit still.” 

This is the second-order problem that follows custom AI, and it is the one REDEGADES.AI is built to solve. The answer is not less AI. It is aligned AI. A layer that sits on top of the tools your leaders are already using and ensures that when they walk into the room, they are working from the same intelligence. The individual custom AI each leader builds is their competitive edge in their domain. The Decision Alignment Layer is what keeps the company moving in one direction while they use it. 

In five years, there will be companies that used AI casually and companies that structured AI strategically. And inside that second group, there will be companies that stopped at individual AI excellence and companies that aligned it across the leadership team. The first group will be more efficient. The second group will dominate. 

AI as a Co-CxO: More Than Just an Answering Machine

Wade Wyant on June 27, 2026

AI as a Co-CxO: More Than Just an Answering Machine 

How can you get a better ROI on AI? How can you use AI more effectively than your competition? Most executives are already using AI in some form. They open a tool, type a question, and receive a fast response. It might draft an email, summarize a report, or generate a few ideas. That is helpful, but it is not leadership transformation. Those simple steps will not improve strategy, execution, or dramatically increase profit. 

Why are most companies not gaining the maximum value from AI? Unfortunately, AI is often treated like an answering machine, not a major team player. You ask AI a question, it answers, and the interaction ends. There is no memory, no long-term context, and no connection to your strategy. That kind of AI can save time, but it cannot shape direction. 

What’s Different About AI as a Co-CxO? 

AI as a co-CxO is different. As a co-CxO, it can sit at the table with you, understand your business, and help you think through decisions over time. 

The core difference is simple: an answering machine reacts, while a co-CxO thinks with you. An answering machine waits for the next prompt. A co-CxO understands your goals and helps you move toward them. Executives do not need more disconnected answers. They need stronger, more consistent thinking. To get to this point, AI must be trained on your business so it can make the leap from answering machine to co-CxO. 

AI as a Co-CxO Understands Your Business 

Most AI tools today, including platforms from OpenAI, are powerful but general. They are designed to serve millions of users across industries. They do not know your history, your culture, or your priorities. Without that context, the advice they give will always be broad. 

A co-CxO model starts by teaching AI how you think. Every leadership team has a way of making decisions, even if it is never written down. You have core values, strategic priorities, and boundaries you do not cross. When AI understands those patterns, it begins to respond in a way that aligns with your organization. 

For example, if you are disciplined about margin, AI should treat margin as non-negotiable. If culture is your top priority, AI should reflect that in its recommendations. If long-term growth matters more than short-term wins, that bias should be built in. Without this alignment, AI remains generic and disconnected. 

AI as a Co-CxO Remembers Your Company’s History 

Another major shift from answering machine to co-CxO is memory. Leadership conversations happen every week in strategy meetings, planning sessions, and performance reviews. Most of those insights are lost once the meeting ends. A co-CxO captures and organizes those discussions so they can inform future decisions. 

When AI can see patterns across time, it becomes far more valuable. It can highlight recurring issues, surface risks that keep appearing, and point out when strategy is drifting. It can remind you of commitments made last quarter that are quietly being ignored. Human leaders get busy and move on. AI does not. 

This is especially powerful at the C-level because the CEO and other executives are often the constraint in the business. They carry the most responsibility and make the highest-impact decisions. When their thinking improves, the entire organization benefits. Embedding AI at this level influences strategy, not just tasks. 

Start AI with C-Level Executives 

Many companies start AI in marketing or customer service because it feels safer and more contained. Those efforts may improve efficiency, but they rarely change trajectory. A co-CxO approach focuses on leadership first. If you improve decision-making at the top, everything downstream improves. 

To move from answering machine to co-CxO, structure matters. You need a secure environment where company knowledge is stored and organized. You need past decisions, financial data, and strategic plans accessible in one place. With that structure, AI becomes a leadership system rather than a convenience tool. 

Executives do not need to understand the technical details behind AI to lead this shift. They need to understand the leadership opportunity. Ask yourself what decisions you repeat every quarter and what insights get lost in meetings. Then imagine having a consistent partner who remembers all of it. 

AI as a Co-CxO Can Make an Exponential Impact on Your Business 

AI as an answering machine saves minutes. AI as a co-CxO shapes years. 

It preserves institutional memory, reinforces strategy, and challenges blind spots. The leaders who win in this next era will not simply use AI. They will build it into the way they lead. 

One More Question Worth Asking 

There is a step most companies have not considered yet, and it is the one that separates companies that lead from ones that plateau. 

Once your leadership team has embraced the co-CxO model, each leader is sharper, faster, and more confident. That is exactly what you wanted. Here is the part nobody talks about: when each co-CxO has been trained on its owner’s thinking, every leader walks into your next meeting more certain than ever. Each one has a machine that has done their homework, validated their position, and made their case airtight. 

The meetings that used to be a genuine exchange of perspectives become something harder to read. The CEO cannot easily tell a real strategic disagreement from two smart people who are simply drawing their conclusions from different inputs. That is not a failure of the co-CxO model. It is the natural next problem once the model works. 

“Getting each leader a better AI is the right first move. The right second move is making sure they are all working from the same foundation when they get in the room together.” 

The question is not whether AI will be part of your organization. The real question is whether each leader’s AI will stay isolated, or whether the intelligence underneath it will be shared. That is where the next level of ROI lives. 

From 2D to 3D: Custom AI for All, Not Just One-on-One  AI Usage 

Wade Wyant on June 27, 2026

Most leaders today are still living in a two-dimensional AI world. Leaders work with AI in a flat, transactional space, a simple exchange between a person and a tool. You ask a question; it gives an answer. Productivity rises, but perspective doesn’t.  That’s where the revolution begins.

The real power of AI isn’t in what it can do for you as an individual; it’s in what it can do for us as an organization. Moving from 2D to 3D means teaching AI to think like your company, not just like your best prompt engineer. The goal is to transform a single-user interaction into a collective intelligence system. This makes sure AI learns from every voice in your business, weights those inputs appropriately, and synthesizes them into decisions that move the company forward.

The Problem with 2D AI

The two-dimensional AI model is seductive because it’s easy, fast, impressive, and agrees with your input unless properly trained. You type a question into ChatGPT, and in seconds it gives you something useful. Perhaps it’s an email, a summary, or a list of ideas. This may give you a rush of endorphins and increase productivity, but it’s still a flat 2D model. As I tell CEOs, in the 2D world, AI reflects your bias back to you. It agrees with your assumptions. It becomes a mirror, not a multiplier. The real danger is that it can make you more efficient at being wrong.

In a 2D interaction, AI is a tool. Unless trained, AI has no context for your business, your customers, or your leadership DNA. In this instance, what AI doesn’t know can hurt you. What is AI missing just out of the box? This amazing tech doesn’t know which insights matter most, which biases are intentional, or which trade-offs define your culture. So,while it’s helpful for one person, it doesn’t scale across the organization.

Every department ends up building its own siloed use of AI. Marketing builds prompts for branding, and finance builds prompts for analysis. HR builds prompts for policy, and the fractured use of AI extends across the organization. Everyone’s “using AI,” but no one’s connected by it. Sadly, that doesn’t transform the company. Instead, these siloed uses of AI fragment it.

The 3D Shift: From Productivity to Perspective

When we talk about moving to 3D AI, we’re talking about turning individual productivity into organizational perspective. The leap from 2D to 3D AI is the leap from me to we.

In a 3D model, AI captures the wisdom, data, and bias of the entire leadership team, not just the loudest or most technical voices. It integrates the quiet insights, the front-line observations, and the executive strategy into a single system that understands the whole business. AI becomes what I call a living intelligence system.

This is where AI begins to “think with you,” not just “work for you.” At this point, AI can give you contextualized answers, not just generic ones, because it understands your cultur eand your language. The biggest perk is that AI understands your intent. When your leadership team asks AI questions, it responds as if the company itself were answering. That’s the moment AI becomes three-dimensional.

How We Got Here

When we built Redegades, we weren’t trying to create another AI company. We were trying to solve a leadership problem. I saw what was happening inside mid-sized organizations across the United States. People were excited about AI, but the excitement was scattered. Each leader was experimenting alone. Some had brilliant results while others were frustrated. The difference wasn’t their intelligence, but their structure.

So, we started with one premise: AI will only ever be as smart as the system it represents. If the system is flat, then AI will be flat. If the system is dimensional, capturing data, voices, and context, then AI will become dimensional. The solution involved a different perspective, not just more prompts.

We began working with CEOs to structure their organizational data: leadership meeting notes, team insights, key documents, customer patterns, and feedback loops. Once we organized that data into a structured, retrievable format using a custom RAG (retrieval-augmented generation) system, then AI began to behave differently.  AI wasn’t answering like ChatGPT anymore. Instead, AI was answering like the organization.

Custom AI: Thinking Like Your Company

Most people think “custom AI” means hiring coders to build a proprietary model. However, that’s not what we mean at Redegades. The model isn’t the secret sauce. We believe the real power is in the data. You don’t need to build a new brain. You just need to teach the existing one who you are.

Your company’s custom AI is trained on your data. AI ingests your policies, your processes, your playbooks, your transcripts, and your culture. At Redegades, we believe in designing AIto understand your bias, your strategy, and your vocabulary. That’s why I say, “ChatGPT is generic. Your company isn’t.” Generic AI gives you generic answers. Custom AI gives you leadership-aligned answers.

When an organization moves from 2D to 3D, it stops asking “What can AI do for us?” and starts asking “What can AI learn from us?”  That’s the inversion point. That’s the moment when AI becomes a multiplier of leadership instead of a mirror of convenience.

Capturing Every Voice

The heart of the 3D system is voice because intelligence is born from conversation. In every business, there are voices that dominate and voices that disappear. The CEO speaks loudly, but the strategist speaks clearly. A practical voice comes from the operations manager. Yet, the person who sees the customer every daily, often the one with the sharpest insights, stays quiet. AI gives you the chance to capture all these voices.  As I tell clients, the quiet voices in your company often hold the loudest truths.

From Meetings to Models

Every meeting your team has is filled with data that is waiting to become useful intelligence. Think about all the hours of conversation, insights, decisions, and emotional cues. In the 2D world, this is all lost the moment the meeting ends. However, in the 3D world, the valuable information is captured, transcribed, analyzed, and structured.

Your AI can summarize key points, identify recurring themes, track who contributes what, and connect decisions to outcomes. Over time, it builds a real-time leadership knowledge base, a digital model of how your company thinks, learns, and decides. That model becomes the foundation of your co-CEO system. AI becomes a living brain that grows with you.        From Flat Tools to Living Systems

In the 2D world, AI is an assistant. In the 3D world, AI is an advisor. A 2D assistant responds when spoken to. A 3D advisor observes, remembers, and anticipates. It connects dots you didn’t even know were related.

That’s why I say the shift from 2D to 3D isn’t about technology. The real shift is about leadership. It requires humility to admit that your perspective is only one dimension of the truth. It requires discipline to capture every other dimension around you. When leaders make that shift, their organizations transform. AI stops being an experiment and starts being a culture.

The Flywheel Effect

The most powerful outcome of 3D AI is momentum. Once your intelligence system is structured (data, feedback, and voice all connected), it begins to accelerate itself. Each interaction provides new data for training. Each correction improves future results, and each decision adds context. That’s the process for companies moving from using AI to becoming AI-driven.

As I often remind leaders, in the 3D world, AI isn’t a project. It’s a participant. Your co-CEO doesn’t clock out at 5 p.m. It keeps learning, adjusting, and building the flywheel. The organization begins to operate as one connected, thinking entity. Leadership, data, and AI all spin in sync.

Why It Matters Now

Because we’re in the first era where leadership itself is being digitized, the shift to 3D implementation of AI is necessary to gain the competitive edge. If you stay in 2D, you’ll soon find yourself competing with companies that think in 3D, and that’s not a fight you can win.

A 3D company learns faster, executes faster, and scales smarter. Instead of relying on memory, 3D companies rely on a connected and trained AI. With a 3D version, your company doesn’t debate assumptions; it uses AI to analyze evidence. Connecting AI and the company into a 3D model allows AI to keep working even when you’re not. AI doesn’t wait for meetings; it makes progress continuously. That’s what happens when you move from isolated intelligence to collective intelligence. You stop playing defense and start shaping the future.

The difference between 2D and 3D is a philosophy, not a feature. Two-dimensional AI is transactional, but 3D is transformational. In 2D AI, an individual works with AI alone, but in a 3D model, a collective group of people are giving and receiving feedback.  Two-dimensional AI gives you answers, but three-dimensional AI gives you awareness.

Most companies are still living in two dimensions, where everything is flat and efficient, but fragile. The future belongs to those willing to build the third dimension. In that third dimension lies the greatest competitive advantage of all: a company that truly thinks for itself.

Why AI Should Think Like You

Wade Wyant on June 27, 2026

Why AI Should Think Like You 

Most companies are building AI systems that are incredibly intelligent, but they remain strangely disconnected from how their leaders actually think. Executives today are experimenting with tools like ChatGPT, Copilot, or Gemini, hoping they will unlock faster decisions, sharper insights, and better strategy. Yet many of these systems feel generic. They produce good answers, but not your answers. They analyze data, but not through your lens. The result is AI that is powerful but oddly impersonal. With generic AI, your AI system is more like a consultant who just arrived than a trusted advisor who understands your business. 

The real breakthrough for leaders will not come from simply using AI more often. It will come from building an AI system that thinks the way you think. 

The Hidden Problem With “Generic” AI 

Most AI systems are trained on massive amounts of public information, such as articles, websites, books, and datasets from across the internet. This gives them broad knowledge, but it also means they approach problems from a very generalized perspective. 

That works well for answering questions like “What are the benefits of supply chain diversification?” or “What are common marketing strategies for SaaS companies?” Yet, executives rarely make decisions in a generic environment. 

Your company has its own risk tolerance, and your leadership team has its own culture. The strategy for your business reflects years of experience, intuition, and lessons learned. 

When AI lacks this context, its recommendations can feel technically correct but strategically off. It might suggest ideas that contradict how your business operates or overlook the subtle dynamics inside your organization. 

This is why many AI experiments stall. The technology is impressive, but the advice feels detached from reality. 

Leadership Thinking Is a Strategic Asset 

Every successful company develops a unique decision-making pattern over time. Some leaders prioritize aggressive growth. Others emphasize operational efficiency. Some value experimentation and risk-taking, while others build businesses on discipline and predictability. These patterns are not random. They are the accumulated wisdom of leadership. 

They come from years of experience, market lessons, strategic frameworks, company culture, and leadership instincts. 

In traditional organizations, this knowledge lives inside people’s heads. When leaders leave, retire, or move on, much of that thinking leaves with them. One of the most powerful uses of AI is the ability to capture and digitize that leadership intelligence. Instead of being lost or diluted, the strategic thinking of the organization becomes part of the system itself. 

The Idea of a “Digital Leadership Mind” 

Imagine an AI system that does not just answer questions. Rather, it answers them the way your leadership team would. For example, when evaluating an acquisition, it understands your company’s acquisition philosophy. When reviewing strategy, it reflects the frameworks your organization believes in. When analyzing risk, it considers the tolerance level your leadership has historically used. This concept is sometimes described as creating a digital version of leadership thinking. 

Rather than replacing executives, the AI becomes a thought partner, an always-available advisor trained on how your organization thinks. Some leaders jokingly describe this as cloning themselves. AI is the closest technology we have ever had to making that possible. 

Why Bias Is Not a Bad Word in Business 

In the world of AI ethics, the word bias often carries negative connotations. But in business strategy, bias can be extremely valuable. 

Every company operates with a set of strategic biases: how aggressive you are in pricing, how quickly you enter new markets, how much risk you tolerate, how you balance growth versus profitability. 

These biases shape the identity of your business. Without them, decisions become generic. Generic decisions rarely produce exceptional companies. 

When AI is trained on your leadership thinking, such as your frameworks, priorities, and strategic philosophy, it begins to operate within those same boundaries. It does not simply provide an answer. It provides an answer aligned with how your organization thinks. This is where AI becomes more than a tool. It becomes a strategic extension of leadership. 

Capturing the Intelligence Already Inside Your Company 

One of the biggest missed opportunities in business is how much knowledge disappears after meetings. Leadership teams gather in rooms every week, and ideas are debated while insights are shared. In these meetings, important strategies are formed. Then the meeting ends, and most of that thinking vanishes. Even with notes and slides, the full richness of the discussion is rarely captured. 

Modern AI systems can record and analyze these conversations, identifying patterns, ideas, and insights that might otherwise be lost. Over time, this creates a living knowledge base of how the company thinks and operates. Instead of leadership intelligence fading over time, it compounds. The more conversations the system learns from, the better it becomes at understanding the organization. 

The Difference Between Public AI and Custom AI 

This is where the distinction between public AI tools and custom AI systems becomes critical. Public AI tools are incredibly useful, but they operate with a generalized worldview. 

Custom AI systems are trained on your organization’s leadership thinking, internal data, industry context, and strategic frameworks. In other words, they understand your company the way an experienced executive would. Many organizations begin their AI journey using public tools, which is a great starting point. Yet, the real strategic advantage often comes from building systems that are uniquely aligned with how the business operates. When that happens, AI stops feeling like an external service and starts functioning as part of the leadership team. 

The Competitive Advantage of Digitized Leadership 

Businesses have always tried to scale leadership thinking. Consultants write playbooks, and companies build training programs. Leaders even mentor future executives. AI introduces a new possibility: scaling leadership intelligence directly through technology. 

When leadership thinking becomes digitized, new employees learn faster, decisions become more consistent, insights become easier to access, and institutional knowledge is preserved. 

Perhaps most importantly, the organization becomes less dependent on a single individual. The knowledge that once lived in one leader’s head becomes accessible to the entire company. 

The Next Problem: When Every Leader’s AI Thinks Like Them 

There is a step beyond this that most companies have not thought through yet, and it is the one that will separate the companies that lead from the ones that stall. 

Teaching AI to think like your organization is the right move. The problem surfaces when every leader on your team does it. Your CFO builds an AI that thinks like her. Your CMO builds one that thinks like him. Your COO and your head of sales both do the same. Each one of them is sharper, faster, and more certain than ever. Each walks into your next leadership meeting armed by a machine that has validated their thinking, sharpened their arguments, and made their position airtight. 

You have not built a smarter leadership team. You have built a team of smarter individuals who agree less. 

The meetings get harder. The debates get louder. The decisions that should take twenty minutes take three rounds. And the CEO can no longer tell a genuine strategic disagreement from two smart people who simply drew their conclusions from different inputs. That is not a people problem. It is an alignment problem. 

“AI does not just make your leaders more productive. When each one is running their own, it quietly privatizes their conviction. The technology that was supposed to get everyone on the same page is building better cases for staying off it.” 

The answer is not to pull back on individual AI. The answer is to add a layer. A layer that sits on top of the tools each leader is already using and aligns the intelligence so that when the room comes together, everyone is working from the same foundation. The debate that follows is real, not manufactured by mismatched inputs. The decisions that come out of it stick, because the whole team was reasoning from the same place. 

AI that thinks like you is the first move. AI that aligns your whole team is the one that changes the game. 

The Future of AI in the Executive Suite 

The future of AI in business will not simply be about automation. Instead, it will be about amplification. Amplifying the thinking of leadership teams, the insights buried inside the organization, and the decisions that shape the next decade. 

In that future, the most successful AI systems will not be the ones with the largest datasets or the most impressive interfaces. They will be the ones that understand the organization using them, and that keep the organization aligned as it grows. The companies that win will not just ask AI for answers. They will teach AI how they think, align that thinking across the full leadership team, and then let it help them think even better.